#import data

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.0      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(ggridges)
weather_df = 
  rnoaa::meteo_pull_monitors(     ##download NOAA weather data
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "TMIN", "TMAX"), 
    date_min = "2017-01-01",
    date_max = "2017-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())
## Registered S3 method overwritten by 'hoardr':
##   method           from
##   print.cache_info httr
## using cached file: ~/Library/Caches/R/noaa_ghcnd/USW00094728.dly
## date created (size, mb): 2022-09-29 10:32:05 (8.401)
## file min/max dates: 1869-01-01 / 2022-09-30
## using cached file: ~/Library/Caches/R/noaa_ghcnd/USC00519397.dly
## date created (size, mb): 2022-09-29 10:32:09 (1.699)
## file min/max dates: 1965-01-01 / 2020-03-31
## using cached file: ~/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly
## date created (size, mb): 2022-09-29 10:32:11 (0.95)
## file min/max dates: 1999-09-01 / 2022-09-30

Let’s make a scatter plot.

ggplot(weather_df, aes(x = tmin, y = tmax))

only make a plot.

ggplot(weather_df, aes(x = tmin, y = tmax)) + 
  geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).

Let’s make a same scatterplot, but different.

weather_df %>%
  drop_na %>% 
  filter(name == "CentralPark_NY") %>%
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point()

Let’ keep making a same scatterplot, but different. (the plot is saved.)

plot_weather = 
  weather_df %>%
  ggplot(aes(x = tmin, y = tmax)) 

plot_weather + geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).

let’s fancy this up a bit.

  ggplot(weather_df, aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name)) +
  geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

按名字区分color 仅在点图中， 因为只在geom_point()

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point(aes()) +
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

write ‘color’ when making plot (the first step), it will be followed in every graphs.

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .3) +
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

‘alpha’ make the graph transparent,

make separate panels. (by facet_grid)

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .3) +
  geom_smooth(se = FALSE) +
  facet_grid(. ~name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

纵向

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .3) +
  geom_smooth(se = FALSE) +
  facet_grid(name ~.)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).

横向

‘tmax’ vs ‘tmin’ is boring, let’s spice it up some.

ggplot(weather_df, aes(x = date, y = tmax, color = name)) + 
  geom_point(aes(size = prcp), alpha = .5) +
  geom_smooth(se = FALSE) + 
  facet_grid(. ~ name) +
  theme(axis.text.x = element_text(angle = 90)) ##rotate the x axis 90'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).

some quick stuff

ggplot(weather_df, aes(x = tmax, y = tmin)) + 
  geom_hex()
## Warning: Removed 15 rows containing non-finite values (stat_binhex).

when the point is too dense, use hex

histograms, barplots, boxplots, violins, …

ggplot(weather_df, aes(x = tmax, fill = name)) + 
  geom_histogram(position = "dodge", binwidth = 2)
## Warning: Removed 3 rows containing non-finite values (stat_bin).

“dodge” separate the bars

MORE OPTIONS!

ggplot(weather_df, aes(x = tmax, fill = name)) + 
  geom_density(alpha = .4, adjust = .5, color = "blue")
## Warning: Removed 3 rows containing non-finite values (stat_density).

boxplot

weather_df %>%
ggplot(aes(x = name, y = tmax)) + geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

violin plots

ggplot(weather_df, aes(x = name, y = tmax)) + 
  geom_violin(aes(fill = name), alpha = .5) + 
  stat_summary(fun = "median", color = "blue")
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing missing values (geom_segment).

density ridges

ggplot(weather_df, aes(x = tmax, y = name)) + 
  geom_density_ridges(scale = .85)
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).

distribution (?scale)

##saving and emdedding plots. First, let’s save a plot.

weather_scatterplot = 
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) + 
  geom_point(aes(size = prcp), alpha = .5) +
  geom_smooth(se = FALSE) + 
  facet_grid(. ~ name) +
  theme(axis.text.x = element_text(angle = 90))

weather_scatterplot
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).

ggsave("./results/weather scatterplot.pdf", weather_scatterplot,
       width = 8, height = 5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).
weather_scatterplot
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).